固体氧化物燃料电池离子- 电子混合传导性Ni/ScSZ 燃料极的制备以及性能分析

在薄膜固体氧化物燃料电池中,为增加发生电化学反应的三相界面面积,燃料极和空气极通常制备成离子-电子混合传导性质的多孔形态。该研究将燃料极电化学反应(氢氧化反应)的催化剂——镍(Ni)和氧离子传导性好的氧化钪稳定的氧化锆(ScSZ)混合制备成离子-电子混合传导燃料极。实验结果显示,与纯 Ni 燃料极相比,所制备的 Ni/ScSZ 燃料极因其三相界面面积的增加,可显著降低极化电阻,但同时欧姆电阻也显著增加,从而最终导致燃料电池单池性能总体下降。     

The value of certification: an empirical assessment of the perceptions of end-users of local area networks

Abstract. Certification is one criterion that employers seek when hiring network professionals. Many employers assume a certified network professional is better able to manage networked resources than is a non-certified counterpart. Therefore, employers often offer a monetary premium to attract certified network professionals. By measuring end-user perceptions, the current study empirically assesses the popular industry assumption that certified network professionals are more adept managers of network resources than those individuals who are not certified network professionals. Analyses of a data derived from 299 end-users of 11 separate local area networks indicate no statistically significant difference exists between the capabilities of certified network professionals and non-certified network professionals.     

Quantum annealing for combinatorial clustering

Clustering is a powerful machine learning technique that groups “similar” data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver “qbsolv.” The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.     

A correlation based cloud-detection algorithm combined with an examination of some split-window assumptions

Twenty-six daytime NOAA-11 AVHRR images covering the Danish waters are analysed together with 99 cloud-free bulk temperature measurements. The images are cloud screened with a cloud-detection algorithm, which in excess of the usual threshold and variation tests consists of: a min_max-routine which among suspicious pixels points out the cloud contaminated pixels, and a separation of cloud pixels from cold water pixels by means of the correlation coefficient between the channel 4 brightness temperature and the channel 4-channel 5 brightness temperature. In the cloud screened images, the channel 4-channel 5 brightness temperatures do not increase with the satellite zenith angle and do not seem to carry any significant information about the true sea surface temperature (SST). Local meteorological and oceanographic effects are believed to be more important than the channel 4-channel 5 brightness temperatures.     

Processing: programming for the media arts

Processing is a programming language and environment built for the media arts communities. It is created to teach fundamentals of computer programming within the media arts context and to serve as a software sketchbook. It is used by students, artists, designers, architects, and researchers for learning, prototyping, and production. This essay discusses the ideas underlying the software and presents its relationship to open source software and the idea of software literacy. Additionally, Processing is discussed in relation to education and online communities.     

Quantifying DNS namespace influence

Name resolution using the Domain Name System (DNS) is integral to today’s Internet. The resolution of a domain name is often dependent on namespace outside the control of the domain’s owner. In this article we review the DNS protocol and several DNS server implementations. Based on our examination, we propose a formal model for analyzing the name dependencies inherent in DNS. Using our name dependency model we derive metrics to quantify the extent to which domain names affect other domain names. It is found that under certain conditions, more than half of the queries for a domain name are influenced by namespaces not expressly configured by administrators. This result serves to quantify the degree of vulnerability of DNS due to dependencies that administrators are unaware of. When we apply metrics from our model to production DNS data, we show that the set of domains whose resolution affects a given domain name is much smaller than previously thought. However, behaviors such as using cached addresses for querying authoritative servers and chaining domain name aliases increase the number and diversity of influential domains, thereby making the DNS infrastructure more vulnerable.     

Using duration models to reduce fragmentation in audio segmentation

We investigate explicit segment duration models in addressing the problem of fragmentation in musical audio segmentation. The resulting probabilistic models are optimised using Markov Chain Monte Carlo methods; in particular, we introduce a modification to Wolff’s algorithm to make it applicable to a segment classification model with an arbitrary duration prior. We apply this to a collection of pop songs, and show experimentally that the generated segmentations suffer much less from fragmentation than those produced by segmentation algorithms based on clustering, and are closer to an expert listener’s annotations, as evaluated by two different performance measures. Editor: Gerhard Widmer     

Comparison of satellite-derived and in-situ observations of ice and snow surface temperatures over Greenland

The most practical way to get spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived “clear-sky” LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air temperature observations from Greenland Climate Network (GC-Net) automatic weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from ? 40 to 0 °C. The satellite-derived LSTs agree within a relative RMS uncertainty of ~ 0.5 °C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a “point” while the satellite instruments record data over an area varying in size from: 57 × 57 m (ETM+), 90 × 90 m (ASTER), or to 1 × 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty ~ 2 °C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.     

Physical and functional interactions between the herpes simplex virus UL15 and UL28 DNA cleavage and packaging proteins

Herpes simplex virus (HSV) DNA is cleaved from concatemers and packaged into capsids in infected cell nuclei. This process requires seven viral proteins, including UL15 and UL28. UL15 expressed alone displays a nuclear localization, while UL28 remains cytoplasmic. Coexpression with UL15 enables UL28 to enter nuclei, suggesting an interaction between the two proteins. Additionally, UL28 copurified with UL15 from HSV-infected cells after ion-exchange and DNA affinity chromatography, and the complex sedimented as a 1:1 heterodimer upon sucrose gradient centrifugation. These findings are evidence of a physical interaction of UL15 and UL28 and a functional role for UL15 in directing UL28 to the nucleus.